1. Field of the Invention
The present invention relates to signal transmission and detection, and in particular, to techniques for compensating for signal distortions caused by signal dispersion and nonlinearities within the signal transmission media.
2. Description of the Related Art
Signal processing architectures for intersymbol interference (ISI) equalization as used for communications transmission and/or storage systems may be divided into two categories: discrete-time architecture and continuous-time architecture. Discrete-time architectures, commonly used in current systems, use a sampled approach to render the input continuous-time, analog waveform in discrete form. Typically, a high resolution A/D converter, which follows the analog anti-aliasing filter, is used as the sampler at the analog front end. Continuous-time architectures use an analog continuous-time approach which directly processes and equalizes the incoming analog waveform while remaining in the continuous time domain until the final data bit stream is generated.
At present, those signal processing architectures having a feedforward transversal filter and a feedback filter as their basic components are considered, and in particular, the following scenarios: discrete-time/continuous-time architectures with fractionally-spaced (i.e., tap spacing less than symbol-spaced) feedforward taps; continuous-time architecture with feedback that is nominally symbol-spaced; continuous-time/discrete-time architectures with fractionally-spaced feedback. For purposes of the presently claimed invention, the following discussion concerns “fat tap” adaptation to cover the continuous-time architecture with fractionally-spaced feedback; however, such discussion may be readily extended to cover the other scenarios as well.
Fractional-spaced feedforward filters have commonly been used either as stand-alone linear equalizers or in combination with Decision Feedback. Advantages of fractional-spaced versus symbol-spaced feedforward filters include: added robustness to constant or slowly varying sampling phase offset or sampling jitter; and improved signal-to-noise ratio (SNR) sensitivity, particularly in the absence of complete channel information, due to the role of the fractional-spaced filter as a combined adaptive matched filter and equalizer.
The adaptation technique for the tap coefficients have always implicitly assumed independence in the adaptation of the successive tap coefficients, which has been based on minimizing the mean squared error (MSE) as computed using the difference between the slicer input and output. This adaptation technique is referred to as LMSE (least mean squared error) or MMSE (minimized mean squared error) adaptation. It can be shown that the LMSE adaptation for both fractional feedforward or symbol spaced feedback at iteration k+1 reduces to the following coefficient update equations:c(k+1)=c(k)+μe(k)s (discrete-time adaptation case)where c(k) is the tap coefficient vector and e(k) the corresponding error at the kth iteration, s is the vector with components as the input waveform to the corresponding tap mixer and μ is a constant and is an adaptation parameter; and
      c    _    =            ∫      0      t        ⁢                  μ        ·                  e          ⁡                      (            t            )                              ⁢                        s          _                ⁡                  (          t          )                    ⁢                          ⁢              ⅆ                  t          ⁡                      (                          continuous              ⁢                              -                            ⁢              time              ⁢                                                          ⁢              adaptation              ⁢                                                          ⁢              case                        )                              with similar terminology as above.
When continuous-time feedback is considered, a number of difficulties are encountered. For example, it is difficult and sometimes unfeasible to design precisely symbol-spaced, flat group delay filters. If the total group delay on the feedback path for canceling successive past symbols is even slightly different from the corresponding symbol period, the performance loss can be substantial. This may necessitate the need for using fractionally-spaced feedback filters. Fractionally-spaced feedback filters may also be needed to account for the slicer-induced jitter and/or the data bits pattern-specific group delays due to frequency dependent group delays of the slicer, mixer and any other analog/digital component on the feedback data path. Further when an equalizer with fractionally-spaced feedback taps is used, independent LMS adaptation of the successive feedback taps fails because of the strongly correlated nature of the fractional tap-spaced feedback data.